A general probabilistic framework for clustering individuals and objects
Igor V. Cadez, Scott J. Gaffney, Padhraic Smyth · 2000
This paper presents a unifying probabilistic framework for clustering individuals or systems into groups when the available data measurements are not multiv ariate v ectors of xed dimensionality.For example, one might h a ve data from a set of medical patien ts,where for each patien tone has a set of of observed time-series, each time-series of potentially dierent length and dierent sampling rate.We propose a general model-based probabilistic framework for clustering data types of this form whic hare non-v ectorin nature and may vary in size from individual to individual.The Expectation-Maximization (EM) procedure for clustering within this framework is discussed and w e discuss ho w it be applied in a general manner to clustering of sequences, time-series, trajectories, and other non-vector data.We sho w that a number of earlier algorithms can be viewed as special cases within this unifying framework.The paper concludes with several illustrations of the method, including clustering of red blood cell data in a medical diagnosis context, clustering of proteins from curves of gene expression data, and clustering of individuals based on their sequences of Web na vigation.